In general, image compression seeks to reduce the storage requirements for an image. Decompression restores the image. Not all compression/decompression processes restore images to their original form. Those which do are called "lossless" methods. In general, lossless methods do not compress images as highly as do lossy methods which change the image and introduce some degradation in image quality. In applications where high-compression ratios are desired, lossy methods are used most frequently.
Images can be compressed as they contain spatial correlation. This correlation implies that differences in neighboring pixel values are small compared to the dynamic range of the image. A basic rule-of-thumb is that more correlation implies a greater potential for higher compression ratios without loss of visual image fidelity. The vast majority of image compression methods have their foundations in broad statistical measures. Some methods are more sophisticated and vary the compression algorithm based upon local statistics (see M. Rabbani and J. P. Jones, "Digital Image Compression Techniques," vol., T77, SPIE Press, Bellingham, Wash., 1991). However, all of these techniques are applied to the entire image as there is no prior knowledge of image features and image position. The statistics account for correlations between neighboring pixels; they do not account for correlations between groups of pixels in corresponding locations of different images.
Compression algorithms have been developed to handle motion sequences of images such as sequential frames of a motion picture (see Bernd Jahne, "Digital Image Processing: Concepts, Algorithms, and Scientific Applications", Springer-Verlag, Berlin, 1991). Images taken close in time have a high degree of correlation between them, and the determination of the differences between the images as the movement of the image segments leads to large compression ratios. This type of image-to-image correlation works well for images which undergo incremental distortions.
Other collections of images have image-to-image correlation, but not to the degree that motion sequences possess and do not compress well with motion algorithms. Consider a library of pictures of missing children. For this collection of images, there will be a large degree of image-to-image correlation based upon pixel location as faces share certain common features. This correlation across different images, just as with the spatial correlation in a given image, can be exploited to improve compression.
Analysis of image libraries yields knowledge of the relative importance of image fidelity based on location in the images. Indeed, maintaining good image fidelity on the face of a child would be more important than fidelity in the hair or shoulders which in turn would be more important than the background. Image compression can be more aggressive in regions where visual image fidelity is less important.
In many applications, preserving the orientation and quantization of the original image is less important than the maintaining of the visual information contained within the image. In particular, for images in the missing children library, if the identity of the child in the portrait can be ascertained with equal ease from either the original image or an image processed to aid in compression, then there is no loss in putting the processed image into the library. This principle can be applied to build the library of processed images by putting the original images into a standardized format. For missing children portraits, this might include orienting the head of each child to make the eyes horizontal centering the head relative to the image boundaries. Once constructed, these standardized images will be well compressed as the knowledge of their standardization adds image-to-image correlation.
Techniques from a compression method known as vector quantization (VQ) are useful in finding correlation between portions of an image. Compression by vector quantization VQ is well-suited for fixed-rate, lossy, high-ratio compression applications (see R. M. Gray, "Vector Quantization," IEEE ASSP Magazine, Vol. 1, April, 1984, pp. 4-29). This method breaks the image into small patches or "image blocks." These blocks are then matched against other image blocks in a predetermined set of image blocks, commonly known as the codebook. The matching algorithm is commonly the minimum-squared-error (MSE). Since the set of image blocks is predetermined, one of the entries of the set can be referenced by a simple index. As a result a multi-pixel block can be referenced by a single number. Using such a method the number of bits for an image can be budgeted. When a greater number of bits is allocated per image block, either the size of the codebook can be increased or the size of the block can be made smaller.
A need has therefore been felt for a technique and associated apparatus for improving the compression of data representing images and, particularly facial images. This technique would be particularly important in areas where limited storage space is available to represent a facial image, such as on a transaction card.